Face Detection Using Radial Basis Functions Neural Networks With Fixed Spread
This paper presented a face detection system using Radial Basis Function Neural Networks With Fixed Spread Value. Face detection is the first step in face recognition system. The purpose is to localize and extract the face region from the background that will be fed into the face recognition system for identification. General preprocessing approach was used for normalizing the image and Radial Basis Function (RBF) Neural Network was used to distinguish between face and non-face. RBF Neural Networks offer several advantages compared to other neural network architecture such as they can be trained using fast two stages training algorithm and the network possesses the property of best approximation. The output of the network can be optimized by setting suitable value of center and spread of the RBF. In this paper, fixed spread value will be used. The Radial Basis Function Neural Network (RBFNN) used to distinguish faces and non-faces and the evaluation of the system will be the performance of detection, False Acceptance Rate (FAR), False Rejection Rate (FRR) and the discriminative properties.
Code (0)
등록된 구현이 없습니다.
Tasks
Face DetectionFace RecognitionSimilar Papers 제목 키워드 기반
Rotation Invariant Face Detection Using Wavelet, PCA and Radial Basis Function Networks
This paper introduces a novel method for human face detection with its orientation by using wavelet, principle component analysis (PCA) and redial basis networks. The input image is analyzed by two-dimensional wavelet an…
Face DetectionNeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions
We present a novel type of neural fields that uses general radial bases for signal representation. State-of-the-art neural fields typically rely on grid-based representations for storing local neural features and N-dimen…
LISR: Learning Linear 3D Implicit Surface Representation Using Compactly Supported Radial Basis Functions
Implicit 3D surface reconstruction of an object from its partial and noisy 3D point cloud scan is the classical geometry processing and 3D computer vision problem. In the literature, various 3D shape representations have…
Surface ReconstructionCascaded Light Propagation Volumes using Spherical Radial Basis Functions
This paper introduces a contribution made to one of the newest methods for simulating indirect lighting in dynamic scenes , the cascaded light propagation volumes . Our contribution consists on using Spherical Radial Bas…
Kolmogorov-Arnold Networks are Radial Basis Function Networks
This short paper is a fast proof-of-concept that the 3-order B-splines used in Kolmogorov-Arnold Networks (KANs) can be well approximated by Gaussian radial basis functions. Doing so leads to FastKAN, a much faster imple…
Kolmogorov-Arnold Networks